Book Image

Hands-On ROS for Robotics Programming

By : Bernardo Ronquillo Japón
Book Image

Hands-On ROS for Robotics Programming

By: Bernardo Ronquillo Japón

Overview of this book

Connecting a physical robot to a robot simulation using the Robot Operating System (ROS) infrastructure is one of the most common challenges faced by ROS engineers. With this book, you'll learn how to simulate a robot in a virtual environment and achieve desired behavior in equivalent real-world scenarios. This book starts with an introduction to GoPiGo3 and the sensors and actuators with which it is equipped. You'll then work with GoPiGo3's digital twin by creating a 3D model from scratch and running a simulation in ROS using Gazebo. Next, the book will show you how to use GoPiGo3 to build and run an autonomous mobile robot that is aware of its surroundings. Finally, you'll find out how a robot can learn tasks that have not been programmed in the code but are acquired by observing its environment. You'll even cover topics such as deep learning and reinforcement learning. By the end of this robot programming book, you'll be well-versed with the basics of building specific-purpose applications in robotics and developing highly intelligent autonomous robots from scratch.
Table of Contents (19 chapters)
1
Section 1: Physical Robot Assembly and Testing
5
Section 2: Robot Simulation with Gazebo
8
Section 3: Autonomous Navigation Using SLAM
13
Section 4: Adaptive Robot Behavior Using Machine Learning

Technical requirements

In this chapter, we will make use of the code in the Chapter11_OpenAI_Gym folder, located at https://github.com/PacktPublishing/Hands-On-ROS-for-Robotics-Programming/tree/master/Chapter11_OpenAI_Gym. Copy the files of this chapter to the ROS workspace, putting them inside the src folder:

$ cp -R ~/Hands-On-ROS-for-Robotics-Programming/Chapter11_OpenAI_Gym ~/catkin_ws/src/

Next, you will need to install Anaconda (https://www.anaconda.com). This is the Python distribution that has become the de facto open source standard for the Data Science community. It provides a complete Python environment for machine learning projects.

Visit the download section of the Anaconda website at https://www.anaconda.com/distribution/#linux, and select the Python 2.7 bundle. We select this package because the ROS Python client is focused on this version; however, you should be...